Google Cloud Introduces Vertex AI, which makes Machine Learning More Accessible and Useful For Developers and Businesses

Google Cloud | May 19, 2021

Vertex AI, a managed machine learning (ML) platform that helps businesses to accelerate the deployment and maintenance of artificial intelligence (AI) models, becoming generally available today at Google I/O. In comparison to competitive platforms1, Vertex AI takes approximately 80% fewer lines of code to train a model, allowing data scientists and ML engineers of all levels of experience to apply Machine Learning Operations (MLOps) to effectively design and operate ML projects during the development lifecycle.

Data scientists are currently faced with the task of manually piecing together ML point solutions, resulting in a gap in model development and experimentation, with just a few models making it into production. To address these issues, Vertex AI unifies the Google Cloud resources for developing machine learning models into a single UI and API, making the task of building, training, and deploying machine learning models at a scale far easier. Customers can switch models from experimentation to production faster, find trends and anomalies more quickly, make better predictions and decisions, and be more resilient in the face of changing market dynamics in this single environment.

Google has learned valuable lessons about how to create, deliver, and manage machine learning models in production over decades of innovation and strategic investment in AI. These ideas and engineering have been baked into the foundation and nature of Vertex AI, and new Google Research innovation will continue to enrich it. For the first time, data science and machine learning engineering teams can:

• Access Google's AI toolkit, which contains computer vision, language, conversation, and structured data, and is constantly improved by Google Research.

• With new MLOps features like Vertex Vizier, which improves the pace of experimentation, the full run Vertex Feature Store, which helps practitioners serve, share, and reuse ML features, and Vertex Experiments, which accelerates the deployment of models into production with quicker model selection, you can deploy more useful AI applications faster.

• Streamline the end-to-end ML workflow with MLOps tools including Vertex Continuous Monitoring and Vertex Pipelines, which eliminate the complexity of self-service model maintenance and repeatability.

About Google Cloud

With the best infrastructure, platform, industry solutions, and expertise, Google Cloud accelerate companies' ability to digitally transform their businesses. It provides enterprise-grade cloud solutions that take advantage of Google's cutting-edge technologies to help businesses run more efficiently and adapt to evolving needs, laying the foundation for the future. Customers in over 200 countries and territories rely on Google Cloud to help them overcome their most critical business challenges.


Will an artificial intelligence replace the doctor? I think we can look right now and say, at present, the answer is, no. Doctors will use artificial intelligence the way they used diagnostic tools in order to recommend a course of action, and it may heavily be influenced and advised by the artificial intelligence. But, in the end there's a need for a professional between the patient and the system that is able to interpret and override and augment the results of the software.


Will an artificial intelligence replace the doctor? I think we can look right now and say, at present, the answer is, no. Doctors will use artificial intelligence the way they used diagnostic tools in order to recommend a course of action, and it may heavily be influenced and advised by the artificial intelligence. But, in the end there's a need for a professional between the patient and the system that is able to interpret and override and augment the results of the software.

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